Image sensors find applications in a wide variety of fields, including machine vision, robotics, guidance and navigation, automotive applications and consumer products. In many smart image sensors, it is desirable to integrate on-chip circuitry to control the image sensor and to perform signal and image processing on the output image. Charge-coupled devices (CCDs), which have been one of the dominant technologies used for image sensors, however, do not easily lend themselves to large scale signal processing and are not easily integrated with complimentary metal oxide semiconductor (CMOS) circuits.
CMOS image sensors receive light into an imaging array including a photosensitive pixel array. One of the difficulties in designing imaging systems is in the optimization of individual pixels within the pixel array. For example, the imaging system may be affected by lens shading effects due to a combination of imaging lens and image sensor parameters. Examples of lens shading include lens vignetting (typically modeled by a cosine power fourth equation), pixel vignetting (for example due to a variability of chief ray angles (CRA) with respect to the image sensor), light diffraction at the pixel level and crosstalk between pixels. When combined together, these different lens shading phenomena may produce a low frequency change in an amplitude of an imaged flat field, for example, from a center to a corner of the imaging lens. Accordingly, it is desirable to correct a captured image for lens shading effects.
Obtaining an optimized imaging array is becoming increasingly important as technology tends towards producing a reduced pixel size along with an increased image quality. Accordingly, there is an interest in determining optimized shading correction parameters.